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Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

We live in glorious times 100 years from now or even 1000s of years from now, people will look back and wonder, how was it to live in the 21st century. They will wonder, did we realize that we were. They will wonder, did we realize what we were going through and how fundamental the decisions that we made, would be seen in the future. My name is David Orban, and this is the context series for episode nine. There are unique moments in history. And we recognize some of those in our past winter, it is the Renaissance, whether it is the birth or the decline of the Roman Empire. At other times, when both individual and collective decisions have made an impact that reverberated through history, and was remembered as momentous. We are now living a unique moment of transition and transformation that is as important potentially more important than any other time in history. And it is our collective mission to build the basis of inclusivity and empowerment that allows as many people as possible

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to realize that they are an active participant. If they choose to become one in this transition and transformation. Why am I saying that these are glorious times. They are complex, they are dangerous. They are full of uncertainty and unknowns. However, we are equipped with the tools that are required in order to address the challenges that we have in front of us, and to explore the possibilities in order to maximize the probability of desirable outcomes and build the basis of a future that fulfills our potential. The examples are a plan the of the challenges that we are seeing both that we have created, and those that emerge, but we now realize we can face climate change risk of global wars, disruption of social infrastructure. The demographic, dynamic, which, contrary to many assumptions is not about overpopulation. it is a power to the very opposite of not enough people believing in a future where they want their descendants to participate. And as a consequence, choose not to

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procreate. And we end up in a world in decline with fewer and fewer people. These are just a few of our challenges, natural and artificial. And each of these challenges, and many more can and must be addressed. Very is no guarantee that we will find solutions. We cannot be just going. We cannot be just following the marching orders of anyone who pretends that they do have those solutions, and they know if we only attack and destroy some suppose the enemy among us. Then everything will be fine.

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One of the characteristics of our glorious times, is that we have realized that we are all one. There is no human that is an enemy.

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There is no human that is an enemy. We have so many challenges, I don't even want to call them enemies that it would be not only stupid, but dangerous to weaken our own ranks by pointing against any group, and labeling them our enemies. We don't have enough active participants who realize what we can achieve yet. We don't have enough talent, because we allow to squander talent. We need to empower emancipate and excite people all around the world. To make sure not only that they fulfill their individual potential, but that they join the ranks of those who believe that it is worth fighting the challenges in order to overcome them. And there are billions of people who are already learning. What are the tools that are available. Learning that indeed we have methods that are reliable that can analyze what are the solutions, test them out and then implement them and scale them. That is the way to go all over the world in every continent. In every country in every community. The reason why

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the choices that we make. And the way that we address the challenges matter so much in my opinion, is exactly the same reason why we realize that we are one. The power that we have deadlocked is sizable, and the power can disrupt at the fundamental level, Our civilization. If we apply it badly. No. Planet Earth will be fine. Even the global ecosystem will recover, just as it has already recovered from cataclysmic disruptions in the past that destroyed 99 plus percent of the living species. But maybe, humanity will go extinct, maybe for another few billion years, there will be no new species emerging that endowed with self awareness to the degree that we have will have the ability to wonder about its own existence, and develop and apply the tools that we now have. On the other hand, if we use our powers in a direction that is constructive that addresses our challenges. Then we will become a multi planetary species, and it will take hundreds or 1000s of years sooner or later we will

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become. And it will take hundreds or 1000s of years but sooner or later we will become an interplanetary ops. And it will take hundreds or 1000s of years, but sooner or later we will become an interstellar civilization. So we really are at a crossroads. And that is why, in the future of that interstellar civilization 1000s of years from today. When humans and AI AIS and any other label, the self aware, active participants of that civilization will choose to call themselves will look back. They will look back in wonder. And they will ask themselves. How was it to live in those glorious times.

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      Word Count Unique Lemmas Characters Lemmas Density

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      network structure:
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      Network Structure Insights
      mind-viral immunity:
      The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

      In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
      The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
      The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

      Influence Distribution
      Topics Nodes in Top Topic Components Nodes in Top Comp
      Nodes Av Degree Density Weighed Betweenness

      Narrative Influence Propagation:
      The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

      The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

      The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
      propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence) ?   show the chart
      We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

      Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

      For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

      Degree Distribution:
        calculate & show   ?
      (based on kolmogorov-smirnov test) ?   switch to linear
      Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

      This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
      If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

      Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
      ×  ⁝⁝ 
      Main Topical Groups:

      +     full stats   ?  

      The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

      We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
      Most Influential Elements:
      +     Reveal Non-obvious   ?

      We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

      Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

      The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
      Network Structure:

      Reset Graph   Export: Show Options
      Action Advice:
      Structural Gap
      (ask a research question that would link these two topics):
      Reveal the Gap   Generate a Question   ?
      A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

      This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

      Latent Topical Brokers

      These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

      These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

      Emerging Keywords

      Evolution of Topics
      (frequency / time) ?
      The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.

      Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

      Main Topics
      (according to Latent Dirichlet Allocation):

      LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

      We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
      Most Influential Words
      (main topics and words according to LDA):

      We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

      Sentiment Analysis

      positive: | negative: | neutral:
      reset filter    ?  

      We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

      The approach is based on AFINN and Emoji Sentiment Ranking

      Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

      Network Statistics:
      Show Overlapping Nodes Only

      ⤓ Download as CSV  ⤓ Download an Excel File

      Top Relations / Bigrams
      (both directions):

      ⤓ Download   ⤓ Directed Bigrams CSV   ?

      The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

      As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
      Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

      We will build two graphs:
      1) Google search results for your query;
      2) Related searches for your query (Google's SERP);
      Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
      Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

      We will build two graphs:
      1) the content that already exists when you make this search query (informational supply);
      2) what else people are searching for when they make this query (informational demand);
      You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
      Please, enter your query to visualize the search results as a graph, so you can learn more about this topic:

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      Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

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